{"id":"W1994107552","doi":"10.1115/1.3290768","title":"3D Simulation of Manufacturing Defects for Tolerance Analysis","year":2010,"lang":"en","type":"article","venue":"Journal of Computing and Information Science in Engineering","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Machining; Quality (philosophy); Context (archaeology); Reliability engineering; Process (computing); Manufacturing engineering; Engineering; Product (mathematics); Computer science; Engineering drawing; Interval (graph theory); Tolerance analysis; Industrial engineering; Mechanical engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006069994,0.00005854407,0.000139665,0.0007831045,0.00003479623,0.00005840247,0.00009618781,0.00002908357,0.000001260058],"category_scores_gemma":[0.0001237682,0.00005490857,0.00003329457,0.0004175014,0.00001755601,0.001679863,0.00001094599,0.0001189417,7.633757e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001714388,"about_ca_system_score_gemma":0.00001450485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.920363e-7,"about_ca_topic_score_gemma":4.629885e-7,"domain_scores_codex":[0.9992975,0.000001418281,0.0004027907,0.00003493401,0.0001572945,0.0001060358],"domain_scores_gemma":[0.9995454,0.00008915128,0.0001514386,0.00004909395,0.0001290104,0.00003593913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002008687,0.000001532557,0.0004149861,0.0001045881,0.000005444623,5.918222e-8,0.0004951729,0.9904253,0.0004447897,0.00002412339,2.755434e-7,0.008081743],"study_design_scores_gemma":[0.0001516175,0.00001189389,0.01601993,0.00003748865,0.00001170462,0.000002982725,0.00002257606,0.9680462,0.01552914,0.000006796427,0.0001047398,0.00005494693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.542276,0.000007302017,0.4574665,0.00000171932,0.0001734904,0.00002494599,3.26191e-7,0.000009449725,0.000040263],"genre_scores_gemma":[0.980066,0.0000106978,0.0198841,0.000004233574,0.00003109418,3.661777e-7,5.564133e-7,0.000002716836,2.457599e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.43779,"threshold_uncertainty_score":0.2239106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004933490483531811,"score_gpt":0.2279249661803203,"score_spread":0.2229914756967885,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}